A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease

Nicholas J. Ashton, Alejo J. Nevado-Holgado, Imelda S. Barber, Steven Lynham, Veer Gupta, Pratishtha Chatterjee, Kathryn Goozee, Eugene Hone, Steve Pedrini, Kaj Blennow, Michael Schöll, Henrik Zetterberg, Kathryn A. Ellis, Ashley I. Bush, Christopher C. Rowe, Victor L. Villemagne, David Ames, Colin L. Masters, Dag Aarsland, John PowellSimon Lovestone, Ralph Martins, Abdul Hye

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as A negative or A positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict A-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting A-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.

Original languageEnglish
Article numbereaau7220
Number of pages12
JournalScience Advances
Volume5
Issue number2
DOIs
Publication statusPublished - 6 Feb 2019

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